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  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 1 FEATURE SELECTION IN FREQUENCY DOMAIN FOR STATIONARY ECG SIGNAL FOR ECG BEAT CLASSIFICATION R GANESH KUMAR Research Scholar, Department of CSE, Sathyabama University, Chennai, Tamil Nadu 600119, INDIA Dr. Y S KUMARASWAMY Sr. Professor & HOD, Department of MCA, Dayananda Sagar College of Engineering, Bangalore 560078, INDIA ABSTRACT Arrhythmia is a common term used for cardiac rhythm deviating from normal sinus rhythm. Many heart diseases are detected through electrocardiograms (ECG) analysis. Manual analysis of ECG is time consuming and error prone. Thus, an automated system for detecting arrhythmia in ECG signals is essential. In this paper, investigations are carried out to evaluate the performance of soft computing techniques for classifying cardiac arrhythmia. In this study, features are extracted from time series ECG data with Discrete Cosine Transform (DCT) computing the distance between RR waves. The feature is the beat’s extracted RR interval. Frequency domain extracted features are classified using Classification and Regression Tree (CART), Radial Basis Function (RBF), Support Vector Machine (SVM) and Multilayer Perceptron Neural Network (MLP-NN). Experiments were conducted on the MIT-BIH arrhythmia database. Keywords: Cardiac Arrhythmia, Discrete Cosine Transform (DCT), Electrocardiograms (ECG), RR Waves, Soft Computing. INDIAN JOURNAL OF INFORMATION TECHNOLOGY (INDJIT) ISSN 2347-3916 (Print) ISSN xxxx - xxxx (Online) Volume 1, Issue 1, January - June (2014), pp. 01-13 © IAEME: http://www.iaeme.com/INDJIT.asp INDJIT © I A E M E
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 2 1. INTRODUCTION Cardiovascular diseases kill more people than any other diseases. Arrhythmia is a collection term for any cardiac rhythm, which deviates from the normal sinus rhythm. Arrhythmia may be due to a disturbance in impulse formation or conduction, or both, but it is not always an irregular heart activity [1]. Extracting such signal features helps to explain and identify different cardiac arrhythmias. The ECG is an important tool to assess a patient’s cardiac status, as this signal represented the difference between two points on body surface, versus time [2]. This becomes difficult as ECG signal size is large and the presence of many noise types contaminates ECG signals [3].Also manual analysis is time consuming and error prone; thus, extraction of automatic features in ECG signals gains importance. Many tools / algorithms were proposed for feature extraction from ECG signals like total least squares based. Most techniques involve high consumption and processing time for features extraction / classification. Another disadvantage is limited to arrhythmias which are classified. Hence, a new technique is required to classify many arrhythmias. Also, the suggested technique should be amenable for real time implementation to ensure its use in intensive care units / ECG signal collected. Feature selection is the selection of a features subset occurring in a training set and used as features in classification. Feature selection serves two things; it ensures efficiency in training and applying a classifier through decrease of effective feature size. And secondly, feature selection improves classification accuracy through noisy feature elimination. A noisy feature being one that when represented increases classification error in new data. The position and magnitudes of the peaks in ECG such as PR interval, ST interval, QRS interval and QT interval are commonly used to classify arrhythmia [4, 5]. The features are either selected from the time domain or frequency domain [6, 7]. Several methods were suggested for cardiac arrhythmias automatic detection/classification in the literature, including: artificial immune recognition system with fuzzy weighted [8], neural networks [9], fuzzy neural networks [10], wavelet transforms [11, 12], combined wavelet transformation [13], Bayesian classifiers [14] support vector machines [15], and Markov models [16]. This paper investigates performance analysis of soft computing techniques for classifying cardiac arrhythmia using RBF, SVM and MLP-NN. Experiments were conducted on the MIT-BIH arrhythmia database. Features are extracted from time series ECG data with DCT computing the distance between RR waves. The feature is the beat’s extracted RRinterval. The rest of the paper is organized as follows: Section 2 reviews related works available in the literature. Section 3 details the methods and materials used. Section 4 reports the results and Section 5 concludes the paper. 2. RELATED WORKS Karaolis, et al., [17] investigated data-mining systems to assess heart related risk factors and to CHD events. A total of 528 cases from Paphos district in Cyprus was collected for examination, a majority of them being affected with an event. Data-mining analysis was done through the use of c4.5 decision tree algorithm, for the three events applying five different splitting criteria mentioned above. Risk factors considered for classification rules analysis include, 1) MI, age, history of hypertension and smoking; 2) PCI family, hypertension and diabetes histories; 3) for CABG, age, smoking and hypertension history. These risk factors were also extracted by other examiners. For the MI, PCI, and CABG
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 3 models, classification efficiency was 66%, 75%, and 75% respectively. Hence, data-mining system recognizes subjects high and low risk subgroups, and also decides on therapy selection; (i.e.,) whether medical or surgical. Kampouraki, et al., [18] examined SVMs to benefit heartbeat time series (i.e., heart rate signals) classification. Statistical methods and signal analysis techniques extracted signal features. By comparing other neural network-based classification methods, SVM classifier performed eave-one-out type cross validation. Regarding other state-of-the-art classifiers, SVM classifies heart rate signal with reduced low signal-to-noise ratio. Examination was undertaken for two data sets with many features. The first dataset included long-term ECG recordings of juvenile and matured healthy subjects while the second set included long-term ECG recordings of normal subjects classification when compared to the common Heart Rate Variability (HRV) analysis which was unable to classify similar signs correctly. The issue of feature selection is considered in feature space by the classifier. Pasolli, et al., [19] suggested 3 learning strategies to classify ECG Signals by first selecting from a small, suboptimal training with learning strategies then selecting beat samples additionally from unlabeled large set data. Samples are labeled manually and added to the needed training set. The entire process is iterated till the final training set is constructed. Based on support vector machine classification, the proposed strategies are developed on margin sampling, posterior probability and query by committee principles. All proposed strategies show high performance regarding stability and accuracy in relation to a completely random selection strategy (R). Faezipour, et al., [20] presented a repetition-detection concept in a patient-adaptive cardiac profiling system, where a wavelet-based beat-detection mechanism first extracts fiducial ECG points following which a new local ECG beat classifier profiles patients normal cardiac behavior. Beat detection is a hybrid of Pan and Tompkins algorithm and wavelet analysis approach. Experiments were undertaken on a MIT-BIH arrhythmia database proving that this system was able to detect beats with 99.5% accuracy and identify abnormalities with 97.42% accuracy. Kim, et al., [21] proposed an ECG holter system, a signal processing technique comprising of a three-step compression and classification flows with Quad Level Vector (QLV). QLV improved performance with low-computation in ECG processing for both compression and classification flows. Classification was applied to both heartbeat segmentation and R-peak detection. The algorithm’s reliability is upped through a noise robust test. Average compression ratio was 16.9:1 with 0.641% percentage root mean square difference value and a 6.4 kbps encoding rate. Without noise, R-peak detection accuracy performance is 100% and with it, -10dB SNR noise 95.63%. Overall processing cost reduction was by 45.3% through this method. Sufi, et al., [22] presented a new procedure executing real-time CVD (Cardiovascular Disease) classification. This real-time CVD classification automatically informs emergency personnel/hospital through SMS/MMS/e-mail, when a CVD patient has a life-threatening cardiac abnormality. The method uses data mining techniques, like attribute selection (only selects features from compressed ECG) and expectation maximization (EM)-based clustering. Data mining techniques in a hospital server bear constraints in representing each abnormality. A patient’s mobile gets these constraints and a rule-based system that recognizes abnormal beats in real time is used. Experiments undertaken on 50 MIT-BIH ECG entries for cardiac abnormality detection (ventricular flutter/fibrillation, premature ventricular contraction, atrial fibrillation etc.) successfully ended up with 97% average accuracy.
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 4 Neto, et al [23] presented the use of Particle Swarm Optimization (PSO), Wavelets and neural networks for automatic detection of cardiac arrhythmias based on analysis of the ECG. The ECG signal has been evaluated in time-frequency domain using wavelets. Wavelet coefficients were presented as the input of a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) with three layers, which was trained by the PSO algorithm. Then the trained network classified the ECG signal in normal signal, atrial fibrillation or ventricular tachycardia. The database utilized was the MIT-BIH Arrhythmia Database. The accuracy rate was 97.03%. Ramirez, et al [24] described a hybrid architecture for classification of cardiac arrhythmias taking as a source the ECG records MIT-BIH Arrhythmia database. The Samples were taken from the LBBB, RBBB, PVC and Fusion Paced and Normal arrhythmias, as well as the normal beats. These were segmented and normalized and three methods of classification were used: Fuzzy KNN, MLP with Gradient Descent with Momentum Back propagation, and MLP with Scaled Conjugate Gradient Back propagation. Mamdani fuzzy inference system was used to combine the outputs of each classifier, and achieved a very high classification rate of 98%. Abibullaev, et al [25] presented a technique for the detection and classification of cardiac arrhythmias using biorthogonal wavelet functions and SVM. The wavelet transforms was applied to decompose the ECG signal into wavelet scales. Then soft thresholding technique was applied to denoise and detect important cardiac events in the signal. Also SVM classifier was used to discriminate the detected events into normal or pathological ones in the signal. Numeric computations demonstrate that the efficient wavelet pre-processing provides an accurate estimation of important physiological features of ECG and moreover it improves the SVM classification performance. Rua, et al [26] presented the development of two machine learning algorithms on a 32-bit ARM® Cortex® M4 microcontroller core from Freescale Semiconductors. Artificial neural network (ANN) and a SVM were implemented for real time detection of Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF), and they were compared in terms of accuracy. In the feature extraction step a Fast Wavelet Transform (FWT) was used; which was analyzed using the time-frequency characteristics of energy in each sub-band frequency. For the training and validation algorithms, signals from MIT-BIH database with normal sinus rhythm, VF and VT in a time window of 2 seconds were used. Validation results achieve test accuracy of 99.46% by ANN and SVM in VT/VF detection. Mehdi, et al [27] proposed a system to detect cardiac arrhythmia. The techniques were used in pattern recognition which includes: signal pre-processing, filter design analysis and neural network toolboxes in Mat lab environment. Normal and abnormal ECG signals were tested by using recordings obtained from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database. Supervised Radial Basis Function (RBF) network architecture was used for filtration, extraction of morphological features of ECG signal and for classification of abnormal cardiac conditions. Different threshold techniques were implemented to detect best fudicial points on the derivative of filtered ECG signal through signal processing. All fudicial points and intervals extracted were learned to the RBF network for extraction of features through the overall record which was further processed to ANN for classification.
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 5 3. MATERIALS AND METHODS 3.1.MIT-BIH Arrhythmia Database For training and testing of ECG datasets signals for evaluating the performance of the classifiers, Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database is used [28]. Cardiac Arrhythmias for classification include LBBB (Left Bundle Branch Block), RBBB (Right Bundle Branch Block) and normal beats. Each ECG beat used is a matrix (275x1) for one ECG lead with every ECG lead with every ECG signal having five distinct points (P, Q, R, S and T) used for ECG interpretation. 3.2. R-R Interval It is known that the morphology of the QRS complex along with the instantaneous RR interval (interval between two successive R peaks) play an important role in the heart diseases diagnosis [29]. For the classification of an ECG beat the ratio of the RR interval before it to the one after it is a useful feature. The peaks are detected as follows: • The moving average of the signal is calculated using a number of records. • New signal is derived by subtracting the moving average from the original signal. • Peak of the signal ‘R’ is found. • Peaks of P, Q, R, S and T are found by the relative position. The peak amplitude is measured from k line. The k line is given by: k = max(θi, i = 1, 2, …, 11) + c (1) Where θ is the greatest amplitude, type of heartbeat and c is a constant. Figure 1 shows a typical ECG with various peaks and the RR interval. Fig. 1. ECG with R-R interval pattern
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 6 3.3.Discrete Cosine Transform (DCT) Discrete Cosine Transform (DCT) translates time series signal into basic frequency components. The data is pre-processed to find RR intervals using Fast DCT. The FDCT [30], of a lsit of n real number, s(x), x = 0,…., n-1, is the list of length n given by the following equation: ( ) ( ) ( ) ( )1 0 2 1 2 c o s 2 n x x u S u n C u s x n π− = + = ∑ (2) where ( ) 1 2 2C u − = for u=0 or otherwise ( ) 1C u = The constant factors are chosen so that the basis vectors are orthogonal and normalized. The inverse cosine transform (IDCT) is given as: ( ) ( ) ( ) ( )1 0 2 1 2 c o s 2 n x x u S x n C u s u n π− = + = ∑ (3) 3.4.Classifiers 3.4.1. Classification and Regression Trees (CART) Classification and Regression Tree (CART) based on class label produce output that is either classification or regression. If class label is categorical, CART provides a classification tree and regression tree, if it is numeric. A decision tree is created by rules formed on given data’s attribute values. Each rule is based on dependent attribute values. This rule is applied to all nodes till the last node is reached, which is the class node [31]. A Classification/regression tree is a binary tree, that given an input X produces output Ÿ approximating random variable of interest Y, stochastically related to X. this deterministic mapping is as follows. Associated with the tree’s every internal node is a binary function of input X and linked to each external node is specific output label Y. Starting at root node, binary function tests input X. When the result is “0”, left branch is followed; if it results in “1”, then right branch is followed. This is repeated till an external node/leaf is reached when associated label Y is the output. The tree minimizes (approximately) anticipated loss between Y and Ÿ. 3.4.2. Multi-Layer Perceptron Neural Networks (MLP-NN) Neural networks are analytical techniques modelled analogous to learning process in the cognitive system and the brain’s neurological functions. They can predict new observations from earlier ones after executing the learning process by using past data. Neural networks or widely used Multilayer Perceptron Neural Networks (MLPNNs) are computational systems comprising of highly interconnected processing elements sets, called neurons, that process information in response to external stimuli which in turn are transmitted from one processing element to another through synapses/interconnection, which are either excitatory or inhibitory. MLPNNs are used in applications like pattern recognition, and classification [32].
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 7 3.4.3. Support Vector Machines (SVM) Support Vector Machine (SVM) maps input vectors to higher dimensional spaces where maximal separating hyper planes are constructed. Two parallel hyper planes are constructed on both sides of a data searching hyper plane. The separating hyper plane is that which maximizes distance between two parallel hyper planes as shown in Figure 2. It is assumed that larger the margin between parallel hyper planes, better a classifier’s impact. Though SVMs were originally designed for binary classification, it can also be used in multi- class classification issues [33]. Fig. 2. Representation of Hyper planes For a training set consisting of instances (xi; yi), i = 1, …., i, where n ix R∈ and { }1, 1 l y ∈ − , the support vector machines (SVM) has to solve the following optimization problem [19]: Polynomial and Radial Basis Function (RBF) is the different kernel functions used in this study. 1] Polynomial: k (x, x’ ) = (x, x’ )d (4) k (x, x’ ) = ((x, x’ ) + 1)d (5) wx+b=1 wx+b=0 wx’+b=-1
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 8 2] Gaussian Radial Basis Function: A RBF kernel models non-linear relation of class labels and features Radial Basis functions most commonly with a Gaussian form (6) 4. RESULTS AND DISCUSSION Two sets of experiments were conducted, in the first experiment, 153 images were used and in the second with a dataset of 13300 beats from 20 recordings. 4.1 With dataset 153 instances A total of 153 images are used for evaluating the performance of classifiers. The dataset consists of 68 instances of left bunch bundle block, 30 instances of right bunch bundle block and 56 normal instances. In this paper, a method of energy extraction using Discrete Cosine Transform was used and RR interval was extracted and used as feature. Using 10 fold cross validation Classification and Regression tree (CART), Radial Basis Function (RBF), MLP and SVM was tested. Table 1 tabulates the summary of results of classification experiments conducted and the classification accuracy is given in Table 2. Table 1.Summary of Results for various Classifiers Properties CART SVM-RBF kernel SVM Polynomial Kernel MLP NN Correctly Classified Instances 135 141 95 135 Incorrectly Classified Instances 18 12 58 18 Kappa statistic 0.814 0.876 0.3688 0.8141 Mean absolute error 0.1079 0.0523 0.2527 0.0883 Root mean squared error 0.2688 0.2287 0.5027 0.2679 Relative absolute error 25.54 % 12.38 % 59.84 % 20.91 % Root relative squared error 58.52 % 49.78 % 109.44 % 58.33 % Total Number of Instances 153 153 153 153 Table 2. Classification Accuracy Classification Method Classification Accuracy in % CART 88.2353 SVM – RBF kernel 92.16 SVM - Polynomial kernel 62.09 MLP 88.2353 The Figure 3 shows the root mean squared error (RMSE) obtained by various classifiers and the Figure 4 charts the classification accuracy achieved.
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 9 Fig. 3. Root mean squared error (RMSE) achieved Fig. 4. Classification Accuracy for the classifiers used It is seen from Figure 4, that the Gaussian RBF kernel performs better than the Polynomial kernel and MLP. The classification accuracy obtained by RBF kernel is 92.16%. Table 3 tabulates the precision, recall and F measure achieved by the classifiers. The precision, recall and F measure for the techniques used is shown in Figure 5 and 6 respectively. Table 3. Precision, Recall and F-Measure Classification Method Precision Recall F measure CART 0.884 0.882 0.883 SVM – RBF kernel 0.922 0.922 0.922 SVM - Polynomial kernel 0.429 0.621 0.494 MLP 0.882 0.882 0.882 Fig. 5. Precision and Recall Fig. 6. F Measure
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 10 From table 3 and figure 5 it is evident that the precision and recall of RBF kernel is much higher than the other classifiers. The SVM-RBF kernel achieves better precision of 4.3%, 4.54% than CART, and MLP respectively. The SVM-RBF kernel achieves higher recall of 4.54% than CART, 48.47% than SVM-Polykernel and 4.54% than MLP. 4.2 With dataset 13300 beats were taken from 20 recordings Here 13300 beats were taken from 20 recordings. Four additional Markers including sex, Age, Family History and smoker / nonsmoker were considered for the classification problem. The results obtained are tabulated in table 4 and 5. Table 4 Classification accuracy Classification accuracy CART 0.8447 SVM-RBF kernel 0.868 SVM - polykernel 0.8597 MLP 0.8594 Fig. 7 Classification Accuracy Fig. 8 Precision and Recall From table 4 and figure 7 it is observed that the SVM-RBF kernel achieves better accuracy by 2.76% than CART, by 0.97% than SVM-Polykernel and by 1% than MLP. Table 5 Precision and Recall Precision Recall CART 0.7958 0.767067 SVM-RBF kernel 0.838333 0.807333 SVM - polykernel 0.823533 0.793733 MLP 0.826633 0.803
  • Indian Journal of Information Technology (INDJIT), ISSN 2347-3916 (Print), ISSN xxxx - xxxx (Online), Volume 1, Issue 1, January - June (2014), Pp-1-13 ©IAEME 11 From table 5 and figure 8 it is shows that the precision and recall is obtained for various classifiers. The SVM-RBF kernel achieves better precision than other classifiers; by 5.34% than CART, by 1.8% than SVM-Polykernel and by 1.42% than MLP. The SVM-RBF kernel achieves better recall of 5.25% than CART, 1.71% than SVM-Polykernel and 0.54% than MLP. 5. CONCLUSION Cardiac Arrhythmia is an irregular heartbeat due to irregular rhythm caused by the heartbeat slowing down or increasing. ECG is used to assess heart arrhythmia. Different arrhythmia types relevant to heart disease are accurately diagnosed through ECG. ECG’s automatic arrhythmia assessment is widely researched. In this paper, performance analysis of soft computing techniques for classifying cardiac arrhythmia using RBF, SVM and MLP-NN is investigated. Experiments were conducted on the MIT-BIH arrhythmia database. Features like RR interval extracted from ECG are used for arrhythmia classification. For feature extraction, MIT-BIH time series data is converted to frequency domain using Discrete Cosine Transform (DCT). Two arrhythmia types, LBB and RBB were classified with normal beats. Experiments demonstrate Support Vector Machine (SVM) with Gaussian Radial Basis Function (RBF) kernel achieving classification accuracy of 92.16%. In second part of the investigation, 13300 beats were taken from 20 recordings. Four additional Markers including sex, Age, Family History and smoker / nonsmoker were considered for the classification problem. The SVM-RBF kernel achieves best accuracy of 86.8%. REFERENCES 1. Sandoe, B. Sigurd (1991): Arrhythmia – A Guide to Clinical Electrocardiology, Bingen Publishing Partners Verlags GmBH. 2. McSharry, P. E., et al. (2003): A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng., 50(3), pp. 289–294. 3. Singh, B. N.; Tiwari, A. K. (2006): Optimal selection of wavelet basis function applied to ECG signal denoising. Dig. Signal Process., 16(3), pp. 275–287. 4. Christov, G., et al. (2006): Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med. Eng. Phys., 28, pp. 876–887. 5. Chazal, P.; O’Dwyer, M.; Reilly, R. B. (2004): Automatic classification of heart beats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng., 51, pp. 1196–1206. 6. Martinez, J. P.; Olmos, S.; Laguna, P. (2000): Evaluation of a wavelet-based ECG waveform detector on the QT database. Comput. Cardiol., 27, pp. 81–84. 7. Agante, P. M,; Marques de Sa, J. P. (1999): ECG noise filtering using wavelets with soft-thresholding methods. Comput. Cardiol., 26, pp. 535–538. 8. Polat, K.; Şahan, S.; Güneş, S. (2006): A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Systems with Applications, 31(2), pp. 264-269. 9. Wang, J. S., et al. (2012): ECG Arrhythmia Classification Using a Probabilistic Neural Network with a Feature Reduction Method. Neurocomputing.
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